Exosuit historical data

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating, using, or both, exosuit historical data. In some implementations, (i) sensor data generated by sensors of an exosuit worn by a user and (ii) control data indicating actions performed by or control signals generated by the exosuit based on the sensor data while worn by the user are received. The sensor data and the control data are added to a database that includes historical data describing use of the exosuit over time by the user. A control scheme of the exosuit is customized for the user by updating the one or more machine learning models or settings that govern the application of the one or more machine learning models. Forces provided by one or more actuators of the exosuit are controlled using the updated one or more machine learning models or the updated settings.

What is claimed is: 
     
         1 . A computer-implemented method comprising:
 receiving, by one or more computing devices, (i) sensor data generated by sensors of an exosuit worn by a user and (ii) control data indicating actions performed by or control signals generated by the exosuit based on the sensor data while worn by the user, wherein the control data is determined using one or more machine learning models;   adding, by the one or more computing devices, the sensor data and the control data to a database comprising historical data describing use of the exosuit over time by the user;   customizing, by the one or more computing devices, a control scheme of the exosuit for the user by updating the one or more machine learning models or settings that govern application of the one or more machine learning models, wherein the control scheme is customized using the historical data for the user; and   controlling, by the one or more computing devices, forces provided by one or more actuators of the exosuit using the updated one or more machine learning models or the updated settings.   
     
     
         2 . The method of  claim 1 , comprising:
 receiving data that identifies user input that indicates whether an action by the exosuit should have been performed; and   adding the data that identifies the user input to the database, wherein updating the one or more machine learning model comprises updating the one or more machine learning models using the historical data and the user input.   
     
     
         3 . The method of  claim 1 , wherein:
 the one or more machine learning models comprises a generic model; and   updating the one or more machine learning models for the user using the historical data comprises updating the generic model using a transfer learning process and the sensor data received by the exosuit during use of the exosuit by the user.   
     
     
         4 . The method of  claim 1 , wherein updating the one or more machine learning models using the historical data comprises updating the one or more machine learning models using an unsupervised learning process and movement patterns of the user identified from the historical data. 
     
     
         5 . The method of  claim 1 , wherein updating the one or more machine learning models using the historical data comprises updating the one or more machine learning models using a reinforcement learning process and data that identifies user input received from the user who wore the exosuit, the data indicating an assistance measure for the exosuit. 
     
     
         6 . The method of  claim 1 , wherein:
 the database includes second sensor data captured by sensors included in a plurality of other exosuits respectively worn by other users and second control data indicating actions performed by or control signals respectively generated by the other exosuits while worn by the corresponding other users; and   updating the one or more machine learning models comprises updating the one or more machine learning models using the sensor data, the control data, the second sensor data and the second control data that are included in the database of the historical data to obtain the updated one or more machine learning models.   
     
     
         7 . The method of  claim 6 , comprising:
 determining, using some of the sensor data or some of the control data from the database, an exosuit activity type; and   selecting, using the exosuit activity type, a particular machine learning model from a plurality of machine learning models for use by the exosuit when the user is wearing the exosuit.   
     
     
         8 . The method of  claim 7 , wherein the exosuit activity type comprises one of: lifting an object, walking, sitting, standing, running, walking up stairs, or writing. 
     
     
         9 . A non-transitory computer storage medium encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising:
 receiving, by the one or more computers, (i) sensor data generated by sensors of an exosuit worn by a user and (ii) control data indicating actions performed by or control signals generated by the exosuit based on the sensor data while worn by the user, wherein the control data is determined using one or more machine learning models;   adding, by the one or more computers, the sensor data and the control data to a database comprising historical data describing use of the exosuit over time by the user;   customizing, by the one or more computers, a control scheme of the exosuit for the user by updating the one or more machine learning models or settings that govern application of the one or more machine learning models, wherein the control scheme is customized using the historical data for the user; and   controlling, by the one or more computers, forces provided by one or more actuators of the exosuit using the updated one or more machine learning models or the updated settings.   
     
     
         10 . The non-transitory computer storage medium of  claim 9 , comprising:
 receiving data that identifies user input that indicates whether an action by the exosuit should have been performed; and   adding the data that identifies the user input to the database, wherein updating the one or more machine learning model comprises updating the one or more machine learning models using the historical data and the user input.   
     
     
         11 . The non-transitory computer storage medium of  claim 9 , wherein:
 the one or more machine learning models comprises a generic model; and   updating the one or more machine learning models for the user using the historical data comprises updating the generic model using a transfer learning process and the sensor data received by the exosuit during use of the exosuit by the user.   
     
     
         12 . The non-transitory computer storage medium of  claim 9 , wherein updating the one or more machine learning models using the historical data comprises updating the one or more machine learning models using an unsupervised learning process and movement patterns of the user identified from the historical data. 
     
     
         13 . The non-transitory computer storage medium of  claim 9 , wherein updating the one or more machine learning models using the historical data comprises updating the one or more machine learning models using a reinforcement learning process and data that identifies user input received from a user who wore the exosuit, the data indicating an assistance measure for the exosuit. 
     
     
         14 . The non-transitory computer storage medium of  claim 9 , wherein:
 the database includes second sensor data captured by sensors included in a plurality of other exosuits respectively worn by other users and second control data indicating actions performed by or control signals respectively generated by the other exosuits while worn by the corresponding other users; and   updating the one or more machine learning models comprises updating the one or more machine learning models using the sensor data, the control data, the second sensor data and the second control data that are included in the database of the historical data to obtain the updated one or more machine learning models.   
     
     
         15 . A system comprising:
 one or more computers; and   one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
 receiving, by the one or more computers, (i) sensor data generated by sensors of an exosuit worn by a user and (ii) control data indicating actions performed by or control signals generated by the exosuit based on the sensor data while worn by the user, wherein the control data is determined using one or more machine learning models; 
 adding, by the one or more computing computers, the sensor data and the control data to a database comprising historical data describing use of the exosuit over time by the user; 
 customizing, by the one or more computers, a control scheme of the exosuit for the user by updating the one or more machine learning models or settings that govern application of the one or more machine learning models, wherein the control scheme is customized using the historical data for the user; and 
 controlling, by the one or more computers, forces provided by one or more actuators of the exosuit using the updated one or more machine learning models or the updated settings. 
   
     
     
         16 . The system of  claim 15 , comprising:
 receiving data that identifies user input that indicates whether an action by the exosuit should have been performed; and   adding the data that identifies the user input to the database, wherein updating the one or more machine learning model comprises updating the one or more machine learning models using the historical data and the user input.   
     
     
         17 . The system of  claim 15 , wherein:
 the one or more machine learning models comprises a generic model; and   updating the one or more machine learning models for the user using the historical data comprises updating the generic model using a transfer learning process and the sensor data received by the exosuit during use of the exosuit by the user.   
     
     
         18 . The system of  claim 15 , wherein updating the one or more machine learning models using the historical data comprises updating the one or more machine learning models using an unsupervised learning process and movement patterns of the user identified from the historical data. 
     
     
         19 . The system of  claim 15 , wherein updating the one or more machine learning models using the historical data comprises updating the one or more machine learning models using a reinforcement learning process and data that identifies user input received from a user who wore the exosuit, the data indicating an assistance measure for the exosuit. 
     
     
         20 . The system of  claim 15 , wherein:
 the database includes second sensor data captured by sensors included in a plurality of other exosuits respectively worn by other users and second control data indicating actions performed by or control signals respectively generated by the other exosuits while worn by the corresponding other users; and   updating the one or more machine learning models comprises updating the one or more machine learning models using the sensor data, the control data, the second sensor data and the second control data that are included in the database of the historical data to obtain the updated one or more machine learning models.